Paper Info
Title
Autonomous real-time landing site selection for Venus and Titan using Evolutionary Fuzzy Cognitive Maps
Abstract
Future science-driven landing missions, conceived to collect in situ data on regions of planetary bodies that have the highest potential to yield important scientific discoveries, will require a higher degree of autonomy. The latter includes the ability of the spacecraft to autonomously select the landing site using real-time data acquired during the descent phase. This paper presents the development of an Evolutionary Fuzzy Cognitive Map (E-FCM) model that implements an artificial intelligence system capable of autonomously selecting a landing site with the highest potential for scientific discoveries constrained by the requirement of soft landing in a region with safe terrains. The proposed E-FCM evolves its internal states and interconnections as a function of real-time data collected during the descent phase, therefore improving the decision process as more accurate information becomes available. The E-FCM is constructed using knowledge accumulated by planetary experts and it is tested on scenarios that simulate the decision process during the descent phase toward the Hyndla Regio on Venus. The E-FCM is shown to quickly reach conclusions that are consistent with what would be the choice of a planetary expert if the scientist were presented with the same information. The proposed methodology is fast and efficient and may be suitable for on-board spacecraft implementation and real-time decision making during the course of robotic exploration of the Solar System.
Year
DOI
Venue
2012
10.1016/j.asoc.2012.01.014
Appl. Soft Comput.
Keywords
Field
DocType
landing site,real-time data,decision process,soft landing,highest potential,planetary body,autonomous real-time landing site,future science-driven landing mission,proposed e-fcm,planetary expert,evolutionary fuzzy cognitive maps,descent phase,autonomous systems,fuzzy cognitive maps
Fuzzy cognitive map,Terrain,Site selection,Artificial intelligence,Autonomous system (Internet),Soft landing,Venus,Artificial Intelligence System,Machine learning,Mathematics,Spacecraft
Journal
Volume
Issue
ISSN
12
12
1568-4946
Citations 
PageRank 
References 
2
0.37
11
Authors
3
Name
Order
Citations
PageRank
Roberto Furfaro143.48
W. Fink2123.05
Jeffrey S. Kargel3112.97